Volumetric data processing and visualization play an important role in medical applications. Depending on the application, various visualization techniques may be employed. For example, direct volume rendering has been used for medical images, such as those acquired with magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), or any other medical tomographic scanner capable of producing a series of images in a grid-like array.
Many current medical applications demand high resolution volumetric data. The size of such data sets will likely keep increasing at a high rate due to the advance of scan technology. Recent technological advances in the field of tomographic imaging have greatly improved the spatial resolution and speed of data acquisition, resulting in the production of very large datasets composed of hundreds, and even thousands of images. For example, it is possible to rapidly generate a sequence of 1024 images using the Siemens SOMATOM VolumeZoom™ CT scanner, with each image being composed of a grid of 512×512 picture elements, resulting in a three-dimensional volume of 512×512×1024 volume elements (over 268 million data values).
Many toolkits have been proposed for processing and visualizing large volume datasets. For example, the National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) is a software toolkit for performing registration and segmentation of images, while the Visualization Toolkit (VTK) is specifically designed for three-dimensional visualization. However, such toolkits typically handle data processing and rendering separately, and therefore do not allow for the visualization of intermediate results of data processing algorithms.
Direct visualization of segmented or registered data produced during data processing is essential to support doctors in diagnosing illnesses. Therefore, there is a need for a system that integrates data processing and rendering into a single framework to allow for efficient processing and rendering of large volume data and for real-time visualization of the progress of data processing algorithms.